Breast cancer pre- and post-treatment gene expresion anlyses

Introduction

  • Circulating Exosomal microRNAs as Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer
Neoadjuvant chemotherapy (NACT) an increasingly used approach for treatment of breast cancer
Circulating Exosomal microRNAs(miRNA) small RNA molecules circulating in bodily fluids, potentially serving as biomarkers for treatment response after NACT
Pathological Complete Response (pCR) absence of active cancer cells in a tissue from the tumor site after NACT
Non-Pathological Complete Response (non-pCR) presence of active cancer cells in a tissue from the tumor site after the treatment

Introduction

Our Goals of the Study:

  • to compare gene expression and gene interaction networks for pre- and post- treatment

  • to conduct differential expression analysis across pCR and non-pCR groups

The Goal of the Paper:

  • to investigate whether circulating exosomal microRNAs could predict pCR in breast cancer patients treated with NACT

Materials and methods: Flowchart

Methods: Metadata & Describe

Methods: Describe

Methods: Gene Expression Analysis

  • Estimation of Size Factors

  • Estimation of Dispersion

  • Fitting the Negative Binomial GLM

  • Statistical Testing for Differential Expression

  • Multiple Testing Correction

Methods: Virtual pull-down and Gene Enrichement Analysis

  • Virtual pull-down:
library(DiscoNet)
network_pCR <- virtual_pulldown(
  seed_nodes = seed, 
  database = string_database, 
  id_type = "hgnc", 
  string_confidence_score = 700) # default value
  • Community detection
library(DiscoNet)
communities_list <- community_detection(
  graph_object, 
  algorithm = "mcode", # specifies algorithm, in this case MCODE
  D = 0.05, # threshold of weight percentage of the vertex to be use by MCODE standard value
  haircut = TRUE) # removes the nodes that are only connected to a single node in the community detected
  • Over representation analysis
library(fgsea)

ora_result_nonpCR <- fora(
    pathways = biological_process_list, 
    genes = gense_in_each_community, 
    universe = all_genes_in_analysis, 
    minSize = 10
)

Results: Gene Expression Analysis

Results: Virtual pull-down

  • Pre vs. Post Treatment:
    • The nonpCR Group has 5 potential complexes or networks with upregulated or downregulated activity when comparing pre- and post-treatment
    • The pCR Group has 11 potential complexes or networks with upregulated or downregulated activity when comparing pre- and post-treatment
  • Example plots:

Results: Gene Enrichement Analysis

  • For each relevant community in pCR and non_pCR the main enriched biological processes were identified
  • Representative plots:

Discussion and Conclusion

  • Differential gene expression was seen in higher levels in the pCR group
  • Further analysis into the communities found by enrichment analysis
  • Early diagnosis could help patients by:
    • If pCR is achieved: perhaps surgery is not needed
    • If pCR is not achieved: perhaps a different treatment method